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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Discrete-Time Neural Network for Fast Solving Large Linear Estimation Problems and its Application to Image Restoration
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Discrete-Time Neural Network for Fast Solving Large Linear Estimation Problems and its Application to Image Restoration

机译：快速求解大型线性估计问题的离散神经网络及其在图像复原中的应用

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摘要

There is growing interest in solving linear L1 estimation problems for sparsity of the solution and robustness against non-Gaussian noise. This paper proposes a discrete-time neural network which can calculate large linear L1 estimation problems fast. The proposed neural network has a fixed computational step length and is proved to be globally convergent to an optimal solution. Then, the proposed neural network is efficiently applied to image restoration. Numerical results show that the proposed neural network is not only efficient in solving degenerate problems resulting from the nonunique solutions of the linear L1 estimation problems but also needs much less computational time than the related algorithms in solving both linear L1 estimation and image restoration problems.
机译：对于解决方案的稀疏性和针对非高斯噪声的鲁棒性，解决线性L 1 估计问题的兴趣日益浓厚。本文提出了一种离散时间神经网络，可以快速计算大型线性L 1 估计问题。所提出的神经网络具有固定的计算步长，并被证明可以全局收敛到最优解。然后，将所提出的神经网络有效地应用于图像恢复。数值结果表明，所提出的神经网络不仅能够有效地解决由线性L 1 估计问题的非唯一解引起的退化问题，而且比相关算法所需的计算时间要短得多。 L 1 估计和图像恢复问题。

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